Staff Machine Learning Engineer (LiDAR)

About the Role

Key Responsibilities

• Design, implement, and optimize deep learning models for aerial LiDAR data-using CNNs, Transformers, and hybrid architectures
• Build end-to-end pipelines for object detection, segmentation, and terrain modeling from geo-referenced point clouds
• Combine aerial LiDAR with other data sources (e.g., RGB imagery, DSM/DTM, hyperspectral) using cross-modal transformers or fusion models
• Implement efficient 3D spatial reasoning using voxel grids, sparse tensors, and attention-based architectures
• Drive experimentation, model benchmarking, and ablation studies to push accuracy and efficiency boundaries
• Collaborate with geospatial scientists and ML engineers to bring models to production via scalable APIs and cloud-native services
• Apply MLOps practices to manage datasets, monitor model drift, and automate retraining workflows

Requirements

• 7+ years of experience in applied machine learning or computer vision, with 3+ years focused on LiDAR or 3D data
• Proven expertise in 3D deep learning (e.g., 3D CNNs, PointNet/PointNeXt, SparseConvNet, Minkowski Engine)
• Experience building and fine-tuning transformer architectures for spatial or remote sensing applications (e.g., Swin3D, PointTransformer, GeoTransformer)
• Strong coding skills in Python and deep learning libraries (PyTorch preferred)
• Familiarity with aerial LiDAR data characteristics, including waveform/point density, elevation modeling, and coordinate systems (EPSG, UTM, etc.)
• Hands-on experience with geospatial and point cloud libraries (PDAL, Open3D, PCL)
• Understanding of GPU optimization and deployment in production (CUDA, TensorRT, TorchScript)
• Master's or PhD in Computer Science, Remote Sensing, Geomatics, or related field

Nice to Have

• Experience with neural implicit models (e.g., NeRF, occupancy networks) for 3D scene modeling
• Knowledge of spatiotemporal modeling or change detection from periodic LiDAR collections
• Prior work with cloud-based pipelines (AWS SageMaker, GCP Vertex AI, or Azure ML)
• Contributions to open-source geospatial/ML tools or research publications

Qualifications

• Master's or PhD in Computer Science, Remote Sensing, Geomatics, or related field

Benefits & Perks

• Comprehensive Medical, Dental, and Vision Coverage: 100% coverage for employees and 80% for their spouses and children
• Health Reimbursement Account (HRA): 100% funded by AiDASH to cover medical deductibles
• 401(k) Plan: Begin contributing after three months of employment (no company match currently)
• Parental Leave: 16 weeks for primary caregivers and 4 weeks for secondary caregivers
• Generous Vacation Policy: 20 vacation days per year plus an additional flex holiday
• Winter Break: Time off from December 25th through January 2nd